Thesis (Ph. D.)--University of Chicago, Dept. of Mathematics, August 2000. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010. / Committee Member: Alexander Gray; Committee Member: Charles Lee Isbell Jr.; Committee Member: Melody Moore Jackson; Committee Member: Paul M. Corballis; Committee Member: Thad Starner. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Dr. Robert H. Lee, Committee Member ; Dr. Kurt Wiesenfeld, Committee Member ; Dr Robert J. Butera, Committee Member.

We perceive the environment via sensor arrays and interact with it through motor outputs. The work of this thesis concerns how the brain selects actions given the information about the perceived state of the world and how it learns and adapts these selections to changes in this environment. Reinforcement learning theories suggest that an action will be more or less likely to be selected if the outcome has been better or worse than expected. A group of subcortical structures, the basal ganglia (BG), is critically involved in both the selection and the reward prediction. We developed and investigated a computational model of the BG. We implemented a Bayesian-Hebbian learning rule, which computes the weights between two units based on the probability of their activations. We were able test how various configurations of the represented pathways impacted the performance in several reinforcement learning and conditioning tasks. Then, following the development of a more biologically plausible version with spiking neurons, we simulated lesions in the different pathways and assessed how they affected learning and selection. We observed that the evolution of the weights and the performance of the models resembled qualitatively experimental data. The absence of an unique best way to configure the model over all the learning paradigms tested indicates that an agent could dynamically configure its action selection mode, mainly by including or not the reward prediction values in the selection process. We present hypotheses on possible biological substrates for the reward prediction pathway. We base these on the functional requirements for successful learning and on an analysis of the experimental data. We further simulate a loss of dopaminergic neurons similar to that reported in Parkinson’s disease. We suggest that the associated motor symptoms are mostly causedby an impairment of the pathway promoting actions, while the pathway suppressing them seems to remain functional. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p><p> </p>

abstract: Intracortical microstimulation (ICMS) within somatosensory cortex can produce artificial sensations including touch, pressure, and vibration. There is significant interest in using ICMS to provide sensory feedback for a prosthetic limb. In such a system, information recorded from sensors on the prosthetic would be translated into electrical stimulation and delivered directly to the brain, providing feedback about features of objects in contact with the prosthetic. To achieve this goal, multiple simultaneous streams of information will need to be encoded by ICMS in a manner that produces robust, reliable, and discriminable sensations. The first segment of this work focuses on the discriminability of sensations elicited by ICMS within somatosensory cortex. Stimulation on multiple single electrodes and near-simultaneous stimulation across multiple electrodes, driven by a multimodal tactile sensor, were both used in these experiments. A SynTouch BioTac sensor was moved across a flat surface in several directions, and a subset of the sensor's electrode impedance channels were used to drive multichannel ICMS in the somatosensory cortex of a non-human primate. The animal performed a behavioral task during this stimulation to indicate the discriminability of sensations evoked by the electrical stimulation. The animal's responses to ICMS were somewhat inconsistent across experimental sessions but indicated that discriminable sensations were evoked by both single and multichannel ICMS. The factors that affect the discriminability of stimulation-induced sensations are not well understood, in part because the relationship between ICMS and the neural activity it induces is poorly defined. The second component of this work was to develop computational models that describe the populations of neurons likely to be activated by ICMS. Models of several neurons were constructed, and their responses to ICMS were calculated. A three-dimensional cortical model was constructed using these cell models and used to identify the populations of neurons likely to be recruited by ICMS. Stimulation activated neurons in a sparse and discontinuous fashion; additionally, the type, number, and location of neurons likely to be activated by stimulation varied with electrode depth. / Dissertation/Thesis / Videos of neuronal recruitment / Ph.D. Bioengineering 2013

No description available.

Propofol is one of the most commonly used general anesthetics in the world, and yet precisely how it enables loss of consciousness still eludes us. It exhibits rich spectral characteristics on electroencephalogram (EEG) recordings from human patients, including alpha oscillations (8-14 Hz) and Slow Wave Oscillations (SWO, 0.5-2.0 Hz). Additionally, these two oscillations are phase-amplitude coupled (PAC) in a dose-dependent manner: low doses cause “trough-max” coupling where alpha power is maximal during the trough of the SWO cycle, while high doses cause “peak-max” coupling where alpha power is maximal during the peak of the SWO cycle. These propofol rhythms occur at the same frequencies as sleep spindles and sleep SWO, and likely use the same well-studied thalamocortical circuitry. The study of anesthesia therefore represents a safe method for investigating both how our brains sleep and the much-debated components of consciousness. In this dissertation, I use Hodgkin-Huxley-style computational models of both the thalamus and cortex to explain how the direct and indirect effects of propofol can generate such spectral phenomena. In the first part of this dissertation, I discuss results from a thalamic model. I illustrate how GABAA potentiation by propofol can create sustained alpha oscillations in the hyperpolarized thalamus by utilizing the same mechanisms used by sleep spindles. I then show how the thalamus, under artificial SWO conditions, can output trough-max or peak-max PAC depending on background excitation, GABAA potentiation, and H-current conductance. In the second part of this dissertation, I discuss results from a thalamocortical model. My analysis reveals how, in a simulated EEG signal, trough-max PAC can arise from competition between thalamocortical and intracortical synaptic currents, while peak-max PAC can arise from their cooperation. Furthermore, the coherence of cortical SWO rhythms can directly control whether the system expresses trough-max or peak-max PAC, while the indirect effects of propofol on acetylcholine are required for both PAC states. This culmination of years of work reveals just how complex the inner workings of anesthesia can be in enabling its profound effects.

One of the main characteristics of Parkinson's disease (PD) is an exaggerated oscillatory activity in the beta band (12-30 Hz). This activity has been linked to the rise of symptoms such as bradykinesia and akinesia. Even if dopamine replacement therapy (oral intake of dopamine pro-drug levodopa) reverses these symptoms, the effect of the treatment on the beta band activity has still not been completely understood. Therefore, here the temporal dynamics of beta band activity in human patients affected by PD were characterized with and without levodopa treatment. Local-field-potential (LFP) recordings from five patients undergoing dopamine replacement therapy were used. From the LFPs, the extracted beta epochs with significantly higher power than expected from a comparable noisy signal were analyzed. This analysis showed that beta band activity occurred in bursts meaning that high amplitude oscillation alternated with silenced periods. The pathological state also distinguished itself for longer epochs and with power that increased with the length of the epoch. The administration of levodopa reduced the duration of bursts and decreased the overall mean power of the beta band activity. Finally, epochs with the same number of cycles were compared. The Coefficient of Variation prior such epochs suggested that the ongoing activity might lock into a synchronization process prior the burst. These results provide important information to better understand how levodopa alleviates some of the symptoms of PD and pave the way to develop better computational models for the emergence of beta oscillations.

Electroencephalography (EEG) was formerly confined to clinical and research settings with the necessary hardware costing thousands of dollars. In the last five years a number of companies have produced simple electroencephalograms, priced below $300 and available direct to consumers. These have stirred the imaginations of enthusiasts and brought the prospects of "thought-controlled" devices ever closer to reality. While these new devices were largely targeted at video games and toys, active research on enabling people suffering from debilitating diseases to control wheelchairs was being pursued. A number of neurochairs have come to fruition offering a truly hands-free mobility solution, but whether these results could be replicated with emerging low cost products, and thus become a viable option for more people is an open question. This thesis examines existing research in the field of EEG-based assistive technologies, puts current consumer-grade hardware to the test, and explores the possibility of a system designed from the ground up to be only a fraction of the cost of currently completed research prototypes.

The remarkable ability of the human brain to draw an accurate percept from imprecise sensory information is not well understood. Bayesian inference provides an optimal means for drawing perceptual conclusions from sensorineural activity. This approach has frequently been applied to visual and auditory studies but only rarely to studies of tactile perception. We explored whether a Bayesian observer model could replicate fundamental aspects of human tactile spatial perception. The model consisted of an encoder that simulated sensorineural responses with Poisson statistics followed by a decoder that interpreted the observed firing rates. We compared the performance of our Bayesian observer on a battery of tactile tasks to human participant data collected previously by our laboratory and others. The Bayesian observer replicated human performance trends on three spatial acuity tasks: classic two-point discrimination (C2PD), sequential two-point discrimination (S2PD), and two-point orientation discrimination (2POD). We confirmed the widely reported observation that C2PD is the least reliable method of assessing tactile acuity due presumably to the presence of non-spatial cues. Additionally, the Bayesian observer performed similarly to humans on raised letter and Braille character-recognition tasks. The Bayesian observer further replicated two illusions previously reported in humans: an adaptation-induced repulsion illusion and an orientation anisotropy illusion. Taken together, these results suggest that human tactile spatial perception may arise from a Bayesian-like decoder that is unaware of the precise characteristics of its inputs. / Thesis / Master of Science (MSc)